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G0352039045

International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.

G0352039045

1.
International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 3 Issue 5ǁ May 2014 ǁ PP.39-45
www.ijesi.org 39 | Page
Comparative Study of Compression Techniques and Compressed Image
Face Recognition System
1,
Narendra R. Dhoriya , 2,
Devang U. Shah
1,
PG Research Scholar
1,
Dept. of Electronics & communication
RK University, Rajkot, India
2,
Associate Professor Dept. of Electronics & Communication,
RK University, Rajkot, India
ABSTRACT : Now a day, everyone wants to save their memory element more and more by reducing the size of
particular data which they use. So the compression of the data in any field put a great advantage to save the
memory element as well as it will also save the time to transmit the data to others. Face recognition technology
has numerous commercial as well as security applications at various places. For this, requirement of the
memory element or capacity for storage of images is one of the major problems for generation of database for
face recognition system. In this paper image compression techniques using which maximum utilization of
memory element or storage devices can be achieved are discussed. Here techniques used for image compression
for face recognition are DWT & DCT. This research is initial part of developing the algorithm to compare the
results of mostly used DWT & DCT techniques and use one of the best techniques among them for Face
Recognition.
KEYWORDS : DWT, DCT, Image Compression, Face Recognition.
I. INTRODUCTION
Face Recognition technique is one of the most widely used technology in recent era. Now a day we are
moving towards the fast, efficient and most secure network at various places like airport, railway station. For
fast and efficient in the sense of memory contain we required the image size as law as possible. For this purpose
we have to first compress the image and then store it in the database to recognize with captured image.Many
researchers have been made on image compression techniques but still there is a need for compression technique
which provides higher compression with quality reconstruction. Images contain large amount of information
that requires much storage space, large transmission bandwidths and long transmission times, therefore it is
advantageous to compress the image by storing only the essential information needed to reconstruct the image.
A common characteristic of most images is that the neighboring pixels are correlated and therefore contain
redundant information, therefore images having large areas of uniform pixel values will have large
redundancies, and conversely images that have frequent and large changes in pixel values have less redundant
information and harder to compress. Hare we use two different technologies to compress the image, first is
Discrete Wavelet Transform (DWT) and the other is Discrete Cosine Transform (DCT). After compression of
the image it is stored into the database and then can be used as the test data to compare with the trained data
image.
In the discrete wavelet transform the term „wavelet‟ comes from the fact that they integrate to zero;
they wave up and down across the axis. This property ensures that data is not over represented. A signal can be
decomposed into many shifted and scaled representations to that of the original wavelet. To isolate very fine
details in a signal, Very small wavelets can be used; on the other side very large wavelets can identify coarse
details. Here sub-band coding is used to design the DWT filter. This technique collects the signal energy into
few components by isolating the signal characteristics. Then sub-sampling operation is used to decreases the
resolution from one transformation level to the other. In the Discrete Cosine Transform, the finite sequence of
data points in terms of sum of cosine function oscillating at different frequency. The reason behind choosing the
cosine function (not sine function) is that it is more efficient in terms of fewer function are needed to
approximate typical signal. DCT is equivalent to DFT as we only have to transmit few coefficients instead of
whole DCT.The Face Recognition system is one of the best techniques for the feature extraction. The first
historical way to recognize people was based on face geometry. Face identification from a single image is a
challenging task because of variable factors like alterations in scale, location, pose, facial expression, lighting
conditions and overall appearance of the face.

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All face recognition algorithms consists of two parts: a) Face localization & normalization b) Face
identification. Here onwards the use of compressed image (DWT & DCT) is done for creating the database for
face recognition.
II. DISCRETE WAVELET TRANSFORM
Wavelets are signals which are local in time and scale and generally have an irregular shape. A wavelet
is a waveform of effectively limited duration that has an average value of zero. The term „wavelet‟ comes from
the fact that they integrate to zero; they wave up and down across the axis. This property ensures that data is not
over represented. A signal can be decomposed into many shifted and scaled representations of the original
mother wavelet.
A wavelet transform can be used to decompose a signal into component wavelets. Once this is done the
coefficients of the wavelets can be decimated to remove some of the details. Wavelets have the great advantage
of being able to separate the fine details in a signal. Very small wavelets can be used to isolate very fine details
in a signal, while very large wavelets can identify coarse details. The basic idea of the wavelet transform is to
represent any arbitrary function (t) as a superposition of a set of such wavelets or basic functions. These basic
functions or baby wavelets are obtained from a single prototype wavelet called the mother wavelet, by dilation
and translation operation [2].
2.1 DWT Compression Step
 The input images are divided by 8 by 8 or 16 by 16 blocks.
 The image is then applied to the Mapper block.
 Then this mapped image is quantized in different level.
 Symbol Coder is then convert the quantized image into suitable symbols code.
 And then finally we get the compressed image of DWT.
Fig 1: The structure of DWT based compression
For the designing of filters, sub-band coding is used. Sub- band coding is a coding strategy that tries to
isolate different characteristics of a signal in a way that collects the signal energy into few components. This is
referred to as energy compaction. Energy compaction is desirable because it is easier to efficiently encode these
components than the signal. The most commonly used implementation of the discrete wavelet transform (DWT)
consists of recursive application of the low-pass/high-pass one-dimensional (1-D) filter bank successively along
the horizontal and vertical directions of the image. The low-pass filter provides the smooth approximation
coefficients while the high-pass filter is used to extract the detail coefficients at a given resolution.Both low-pass
and high-pass filters are called sub-bands. The number of decompositions performed on original image to obtain
sub bands is called sub-band decomposition level. The high pass sub-band represents residual information of the
original image, needed for the perfect reconstruction of the original image from the low-pass sub-band while the
low pass sub-band represents a down sampled low- resolution version of the original image. It is used for
computer and human vision, musical tone generation, FBI finger print compression.
The filtering step is followed by a sub-sampling operation that decreases the resolution from one transformation
level to the other. After applying the 2-D filler bank at a given level n, the detail coefficients are output, while
the whole filter bank is applied again upon the approximation image until the desired maximum resolution is
achieved. The sub-bands are labeled by using the following symbols [2].
[1] LLn is the approximation image at resolution (level decomposition) n, resulting from low-pass filtering in
the vertical and horizontal directions.
[2] HLn represents the vertical details at resolution n, and results from vertical low-pass filtering and
horizontal high-pass filtering.
[3] LHn represents the horizontal details at resolution n, and results from horizontal low-pass filtering and
vertical high-pass filtering.
[4] HHn represents the diagonal details at resolution n, and results from high-pass filtering in both directions.

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Fig 2: Filtering Sub-band of wavelet transform based compression
III. DISCRETE COSINE TRANSFORM
Compressing an image is significantly different than compressing raw binary data. Of course, general
purpose compression programs can be used to compress images, but the result is less than optimal. DCT has
been widely used in signal processing of image. The one-dimensional DCT is useful in processing one-
dimensional signals such as speech waveforms. For analysis of two-dimensional (2D) signals such as images,
we need a 2D version of the DCT data, especially in coding for compression, for its near-optimal performance.
JPEG is a commonly used standard method of compression for photographic images. The name JPEG stands for
Joint Photographic Experts Group, the name of the committee who created the standard. JPEG provides for
lossy compression of images. Image compression is the application of data compression on digital images. In
effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an
efficient form. The best image quality at a given bit-rate (or compression rate) is the main goal of image
compression. The main objectives of this paper are reducing the image storage space, Easy maintenance and
providing security, Data loss cannot affect the image clarity, Lower bandwidth requirements for transmission,
reducing cost [5].
2.1 DCT Compression Step
 The input images are divided by 8 by 8 or 16 by 16 blocks.
 The two dimensional DCT is computed for each block.
 The DCT coefficient are than quantized, coded, and transmitted.
 The receiver decodes the quantized DCT coefficient; compute the inverse DCT of each block.
 Puts the blocks back together into a single image.
There are eight standard DCT variants, of which four are common. One of the most popular and
comprehensive continuous tone, still frame compression standards is the JPEG standard. In the JPEG base line
coding system which is based on the discrete cosine transform and is adequate for most compression
applications, the input and output images are limited to eight bits, while the quantized DCT coefficient values
are restricted to 11 bits. The discrete cosine transform (DCT) is a mathematical function that transforms digital
image data from the spatial to the frequency domain. For an M x N image, the spatial domain represents the
color value of each pixel. The frequency domain considers the image data as a two dimensional waveform and
represents the waveform in terms of its frequency components. A DCT based method is specified for “lossy‟‟
compression [5].
Fig 3: The structure of DCT based compression

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The input image is N by M. f (i,j) is the intensity of the pixel in row i and column j. F (u, v) is the DCT
coefficient in row k1 and column k2 of the DCT matrix. For most images, much of the signal energy lies at low
frequencies; these appear in the upper left corner of the DCT Compression is achieved since the lower right
values represent higher frequencies, and are often small - small enough to be neglected with little visible
distortion. The DCT input is an 8 by 8 array of integers. This array contains each pixel's gray scale level. 8 bit
pixels have levels from 0 to 255. DCT-based image compression relies on two techniques to reduce the data
required to represent the image. The first is quantization of the image's DCT coefficients; the second is entropy
coding of the quantized coefficients.
Quantization is the process of reducing the number of possible values of a quantity, thereby reducing
the number of bits needed to represent it. Entropy coding is a technique for representing the quantized data as
compactly as possible. We will develop functions to quantize images and to calculate the level of compression
provided by different degrees of quantization. We will not implement the entropy coding required to create a
compressed image file. Eye is most sensitive to low frequencies (upper left corner), less sensitive to high
frequencies (lower right corner) Standard defines 2 default quantization tables, one for luminance (above), and
one for chrominance. Quality factor in most implementations is the scaling factor for default quantization tables.
Custom quantization tables can be put in image/scan header. The purpose of the Zigzag Scan is to group low
frequency coefficients in top of vector. Maps 8 x 8 to 1 x 64 vector. Figure 4 shows the zigzag pattern for the
discrete cosine transform. [1]
Fig 4: Zigzag pattern of DCT
IV. FACE RECOGNITION SYSTEM
Face recognition is emerging as an active research area spanning several disciplines such as image
processing, pattern recognition, computer vision and neural networks. The first historical way to recognize
people was based on face geometry. There are a lot of geometric features based on the points. Face recognition
technology has numerous commercial and law enforcement applications. Below figure 5 shows the basic block
diagram of the face recognition system. [4] In recent years, face recognition has been the subject of intensive
research. With the current perceived world security situation, governments as well as businesses require reliable
methods to accurately identify individuals, without overly infringing on rights to privacy or requiring significant
compliance on the part of the individual being recognized. Face recognition provides an acceptable solution to
this problem. Face recognition has drawn attention of the research community. Face identification from a single
image is a challenging task because of variable factors like alterations in scale, location, pose, facial expression,
occlusion, lighting conditions and overall appearance of the face. With the synergy of efforts from researchers in
diverse fields including computer engineering, mathematics, neuroscience and psychophysics, different
frameworks have evolved for solving the problem of face recognition all face recognition algorithms consists of
two parts: a) Face localization & normalization b) Face identification. Partially automatic algorithms are given a
facial image and the coordinates of centre of eyes. Fully automatic algorithms are only given facial images. A
multitude of techniques have been applied to face recognition like DWT & DCT.
Fig 5: Block diagram of face recognition system

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Table 1: Comparative Analysis of DWT & DCT tech [3]
PARAMETER DWT DCT
Complexity
Less
Complexity
More
Power
More
processing
power
Less
Compression
Ratio
Better
Compression
Ratio
(Adjusted)
Not Adjusted
Information
loss
Less loss of
Information
More loss of
Information
Implementation
Implementation
not Easy
Easy to
Implementation
Coefficients
Well localized
in frequency as
well as spatial
demine
Well localized
in frequency
demine
Block Artifacts
No Block
Artifacts
Distortion of
Media
V. RESULT ANALYSIS
Results obtain through the DWT compression technique is shown in below figure at different level
compression.
Fig 6: Image after first level compression by DWT
Image compression after First level, second level and third level are shown here in fig 6, fig 7 and fig 8
respectively. Moreover fig 9 shows the image compression using DCT.
Fig 7: Image after second level compression by DWT